Index machine

ABSTRACT

In an aspect, provided is a method comprising receiving, at a master node, capability information associated with a plurality of worker nodes, receiving, at the master node, an indexation request, and in response to the indexation request, distributing one or more tasks to the plurality of worker nodes based on the respective capability information, wherein the one or more tasks relate to generating a plurality of indexlets.

CROSS REFERENCE TO RELATED PATENT APPLICATIONS

This application is a continuation of U.S. Non-Provisional applicationSer. No. 15/984,106, filed on May 18, 2018, which claims priority toU.S. Provisional Application No. 62/505,603, filed on May 12, 2017, eachof which are incorporated by reference in their entireties herein.

SUMMARY

It is to be understood that both the following general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive, as claimed. Provided are methods and systemsfor data management and analysis.

In an aspect, provided is a method comprising receiving, at a masternode, capability information associated with a plurality of workernodes, receiving, at the master node, an indexation request, and inresponse to the indexation request, distributing one or more tasks tothe plurality of worker nodes based on the respective capabilityinformation, wherein the one or more tasks relate to generating aplurality of indexlets.

In an aspect, provided is a method comprising receiving, at a globalsymbol master node, capability information associated with a pluralityof worker nodes, receiving, at the global symbol master node, anindexation request, and in response to the indexation request,distributing one or more tasks to the plurality of worker nodes based onthe respective capability information, wherein the one or more tasksrelate to generating a plurality of global symbol maps.

Additional advantages will be set forth in part in the description whichfollows or may be learned by practice. The advantages will be realizedand attained by means of the elements and combinations particularlypointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments and together with thedescription, serve to explain the principles of the methods and systems:

FIG. 1 is a schematic diagram showing an embodiment of a system formingan implementation of the disclosed methods;

FIG. 2 is a set of tables showing exemplary Tables 1-5 of a simpledatabase and associations between variables in the tables;

FIG. 3 is a schematic flowchart showing basic steps performed whenextracting information from a database;

FIG. 4A is tables showing a final data structure, e.g. amultidimensional cube, created by evaluating mathematical functions;

FIG. 4B illustrates an example indexation service;

FIG. 4C illustrates an example global symbol service;

FIG. 5A is a schematic diagram showing how a selection by a useroperates on a scope to generate a data subset;

FIG. 5B is an overview of the relations between data model, indexes indisk and windowed view of disk indexes in memory;

FIG. 5C is a representation of the data structure used for tablehandling;

FIG. 5D is a table tree representation of a data model;

FIG. 5E illustrates an example application of bidirectional tableindexes and bidirectional association indexes;

FIG. 5F is a representation of the data structure used for indexlets;

FIG. 5G illustrates an example of inter-table inferencing usingindexlets;

FIG. 5H illustrates an example of linking indexlets of different tables;

FIG. 5I illustrates an example virtual record;

FIG. 5J illustrates an example method for database traversals;

FIG. 5K illustrates load distribution in a single table case;

FIG. 5L illustrates load distribution in a multiple table case;

FIG. 5M illustrates I2I mapping;

FIG. 6 is a schematic graphical presentation showing selections and adiagram of data associated to the selections as received afterprocessing by an external engine;

FIG. 7 is a schematic representation of data exchanged with an externalengine based on selections in FIG. 6 ;

FIG. 8 is a schematic graphical presentation showing selections and adiagram of data associated to the selections as received after secondcomputations from an external engine;

FIG. 9 is a schematic representation of data exchanged with an externalengine based on selections in FIG. 8 ;

FIG. 10 is a schematic graphical presentation showing selections and adiagram of data associated to the selections as received after thirdcomputations from an external engine;

FIG. 11 is a schematic representation of data exchanged with an externalengine based on selections in FIG. 10 ;

FIG. 12 is a table showing results from computations based on differentselections in the presentation of FIG. 10 ;

FIG. 13 is a schematic graphical presentation showing a further set ofselections and a diagram of data associated to the selections asreceived after third computations from an external engine;

FIG. 14 is a flow chart illustrating an example method;

FIG. 15 is a flow chart illustrating another example method; and

FIG. 16 is an exemplary operating environment for performing thedisclosed methods.

DETAILED DESCRIPTION

Before the present methods and systems are disclosed and described inmore detail, it is to be understood that the methods and systems are notlimited to specific steps, processes, components, or structuredescribed, or to the order or particular combination of such steps orcomponents as described. It is also to be understood that theterminology used herein is for the purpose of describing exemplaryembodiments only and is not intended to be restrictive or limiting.

As used herein the singular forms “a,” “an,” and “the” include bothsingular and plural referents unless the context clearly dictatesotherwise. Values expressed as approximations, by use of antecedentssuch as “about” or “approximately,” shall include reasonable variationsfrom the referenced values. If such approximate values are included withranges, not only are the endpoints considered approximations, themagnitude of the range shall also be considered an approximation. Listsare to be considered exemplary and not restricted or limited to theelements comprising the list or to the order in which the elements havebeen listed unless the context clearly dictates otherwise.

Throughout the specification and claims of this disclosure, thefollowing words have the meaning that is set forth: “comprise” andvariations of the word, such as “comprising” and “comprises,” meanincluding but not limited to, and are not intended to exclude, forexample, other additives, components, integers or steps. “Exemplary”means “an example of”, but not essential, necessary, or restricted orlimited to, nor does it convey an indication of a preferred or idealembodiment. “Include” and variations of the word, such as “including”are not intended to mean something that is restricted or limited to whatis indicated as being included, or to exclude what is not indicated.“May” means something that is permissive but not restrictive orlimiting. “Optional” or “optionally” means something that may or may notbe included without changing the result or what is being described.“Prefer” and variations of the word such as “preferred” or “preferably”mean something that is exemplary and more ideal, but not required. “Suchas” means something that is exemplary.

Steps and components described herein as being used to perform thedisclosed methods and construct the disclosed systems are exemplaryunless the context clearly dictates otherwise. It is to be understoodthat when combinations, subsets, interactions, groups, etc. of thesesteps and components are disclosed, that while specific reference ofeach various individual and collective combinations and permutation ofthese may not be explicitly disclosed, each is specifically contemplatedand described herein, for all methods and systems. This applies to allaspects of this application including, but not limited to, steps indisclosed methods and/or the components disclosed in the systems. Thus,if there are a variety of additional steps that can be performed orcomponents that can be added, it is understood that each of theseadditional steps can be performed and components added with any specificembodiment or combination of embodiments of the disclosed systems andmethods.

The present methods and systems may be understood more readily byreference to the following detailed description of preferred embodimentsand the Examples included therein and to the Figures and their previousand following description.

As will be appreciated by one skilled in the art, the methods andsystems may take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware aspects. Furthermore, the methods and systems may take the formof a computer program product on a computer-readable storage mediumhaving computer-readable program instructions (e.g., computer software)embodied in the storage medium. More particularly, the present methodsand systems may take the form of web-implemented computer software. Anysuitable computer-readable storage medium may be utilized including harddisks, CD-ROMs, optical storage devices, or magnetic storage devices,whether internal, networked or cloud based.

Embodiments of the methods and systems are described below withreference to diagrams, flowcharts and other illustrations of methods,systems, apparatuses and computer program products. It will beunderstood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, respectively, can be implemented by computerprogram instructions. These computer program instructions may be loadedonto a general purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus create a means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including computer-readableinstructions for implementing the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, can be implemented by special purposehardware-based computer systems that perform the specified functions orsteps, or combinations of special purpose hardware and computerinstructions.

The present disclosure relates to computer implemented methods andsystems for data management, data analysis, and processing. Thedisclosed methods and systems can incorporate external data analysisinto an otherwise closed data analysis environment. A typicalenvironment for the systems and methods described herein is forassisting in a computer implemented method for building and updating amulti-dimensional cube data structure, such as, e.g., the systems andmethods described in U.S. Pat. Nos. 7,058,621; 8,745,099; 8,244,741; andU.S. patent application Ser. No. 14/054,321, which are incorporated byreference in their entireties.

In an aspect, the methods and systems can manage associations among datasets with every data point in the analytic dataset being associated withevery other data point in the dataset. Datasets can be larger thanhundreds of tables with thousands of fields. A multi-dimensional datasetor array of data is referred to as an OnLine Analytic Processing (OLAP)cube. A cube can be considered a multi-dimensional generalization of atwo- or three-dimensional spreadsheet. For example, it may be desired tosummarize financial data by product, by time-period, and by city tocompare actual and budget expenses. Product, time, city, and scenario(actual and budget) can be referred to as dimensions. Amulti-dimensional dataset is normally called a hypercube if the numberof dimensions is greater than 3. A hypercube can comprise tuples made oftwo (or more) dimensions and one or more expressions.

FIG. 1 illustrates an associative data indexing engine 100 with dataflowing in from the left and operations starting from a script engine104 and going clockwise (indicated by the clockwise arrow) to exportfeatures 118. Data from a data source 102 can be extracted by a scriptengine 104. The data source 102 can comprise any type of known database,such as relational databases, post-relational databases, object-orienteddatabases, hierarchical databases, flat files, spread sheet, etc. TheInternet may also be regarded as a database in the context of thepresent disclosure. A visual interface can be used as an alternative orcombined with a script engine 104. The script engine 104 can read recordby record from the data source 102 and data can be stored or appended tosymbol and data tables in an internal database 120. Read data can bereferred to as a data set.

In an aspect, the extraction of the data can comprise extracting aninitial data set or scope from the data source 102, e.g. by reading theinitial data set into the primary memory (e.g. RAM) of the computer. Theinitial data set can comprise the entire contents of the data source 102base, or a subset thereof. The internal database 120 can comprise theextracted data and symbol tables. Symbol tables can be created for eachfield and, in one aspect, can only contain the distinct field values,each of which can be represented by their clear text meaning and a bitfilled pointer. The data tables can contain said bit filled pointers.

In the case of a query of the data source 102, a scope can be defined bythe tables included in a SELECT statement (or equivalent) and how theseare joined. In an aspect, the SELECT statement can be SQL (StructuredQuery Language) based. For an Internet search, the scope can be an indexof found web pages, for example, organized as one or more tables. Aresult of scope definition can be a data set.

Once the data has been extracted, a user interface can be generated tofacilitate dynamic display of the data. By way of example, a particularview of a particular dataset or data subset generated for a user can bereferred to as a state space or a session. The methods and systems candynamically generate one or more visual representations of the data topresent in the state space.

A user can make a selection in the data set, causing a logical inferenceengine 106 to evaluate a number of filters on the data set. For example,a query on a database that holds data of placed orders, could berequesting results matching an order year of ‘1999’ and a client groupbe ‘Nisse.’ The selection may thus be uniquely defined by a list ofincluded fields and, for each field, a list of selected values or, moregenerally, a condition. Based on the selection, the logical inferenceengine 106 can generate a data subset that represents a part of thescope. The data subset may thus contain a set of relevant data recordsfrom the scope, or a list of references (e.g. indices, pointers, orbinary numbers) to these relevant data records. The logical inferenceengine 106 can process the selection and can determine what otherselections are possible based on the current selections. In an aspect,flags can enable the logical inference engine 106 to work out thepossible selections. By way of example, two flags can be used: the firstflag can represent whether a value is selected or not, the second canrepresent whether or not a value selection is possible. For every clickin an application, states and colors for all field values can becalculated. These can be referred to as state vectors, which can allowfor state evaluation propagation between tables.

The logical inference engine 106 can utilize an associative model toconnect data. In the associative model, all the fields in the data modelhave a logical association with every other field in the data model. Anexample, data model 501 is shown in FIG. 511 . The data model 501illustrates connections between a plurality of tables which representlogical associations. Depending on the amount of data, the data model501 can be too large to be loaded into memory. To address this issue,the logical inference engine 106 can generate one or more indexes forthe data model. The one or more indexes can be loaded into memory inlieu of the data model 501. The one or more indexes can be used as theassociative model. An index is used by database management programs toprovide quick and efficient associative access to a table's records. Anindex is a data structure (for example, a B-tree, a hash table, and thelike) that stores attributes (e.g., values) for a specific column in atable. A B-tree is a self-balancing tree data structure that keeps datasorted and allows searches, sequential access, insertions, and deletionsin logarithmic time. The B-tree is a generalization of a binary searchtree in that a node can have more than two children. A hash table (alsoreferred to as a hash index) can comprise a collection of bucketsorganized in an array. A hash function maps index keys to correspondingbuckets in the hash index.

Queries that compare for equality to a string can retrieve values veryfast using a hash index. For instance, referring to the tables of FIG. 2, a query of SELECT*FROM Table 2 WHERE Client=‘Kalle’) could benefitfrom a hash index created on the Client column. In this example, thehash index would be configured such that the column value will be thekey into the hash index and the actual value mapped to that key wouldjust be a pointer to the row data in Table 2. Since a hash index is anassociative array, a typical entry can comprise “Kalle=>0x29838”, where0x29838 is a reference to the table row where Kalle is stored in memory.Thus, looking up a value of “Kalle” in a hash index can return areference to the row in memory which is faster than scanning Table 2 tofind all rows with a value of “Kalle” in the Client column. The pointerto the row data enables retrieval of other values in the row.

As shown in FIG. 5B, the logical inference engine 106 can be configuredfor generating one or more bidirectional table indexes (BTI) 502 a, 502b, 502 c, 502 d, and/or 502 e and one or more bidirectional associativeindexes (BAI) 503 a, 503 b, 503 c and/or 503 d based on a data model501. The logical inference engine 106 can scan each table in the datamodel 501 and create the BTI 502 a, 502 b, 502 c, 502 d, and/or 502 e. ABTI can be created for each column of each table in the data. The BTI502 a, 502 b, 502 c, 502 d, and/or 502 e can comprise a hash index. TheBTI 502 a, 502 b, 502 c, 502 d, and/or 502 e can comprise firstattributes and pointers to the table rows comprising the firstattributes. For example, referring to the tables of FIG. 2 , an exampleBTI 502 a can comprise “Kalle=>0x29838”, where Kalle is an attributefound in Table 2 and 0x29838 is a reference to the row in Table 2 whereKalle is stored in memory. Thus, the BTI 502 a, 502 b, 502 c, 502 d,and/or 502 e can be used to determine other attributes in other columns(e.g., second attributes, third attributes, etc. . . . ) in table rowscomprising the first attributes. Accordingly, the BTI can be used todetermine that an association exists between the first attributes andthe other attributes.

The logical inference engine 106 can scan one or more of BTI 502 a, 502b, 502 c, 502 d, and/or 502 e and create the BAT 503 a, 503 b, 503 cand/or 503 d. The BAT 503 a, 503 b, 503 c and/or 503 d can comprise ahash index. The BAT 503 a, 503 b, 503 c and/or 503 d can comprise anindex configured for connecting attributes in a first table to commoncolumns in a second table. The BAI 503 a, 503 b, 503 c and/or 503 d thusallows for identification of rows in the second table which then permitsidentification of other attributes in other tables. For example,referring to the tables of FIG. 2 , an example BAI 503 a can comprise“Kalle=>0x39838”, where Kalle is an attribute found in Table 2 and0x39838 is a reference to a row in Table 4 that contains Kalle. In anaspect, the reference can be to a hash that can be in-memory or on disk.

Using the BTI 502 a, 502 b, 502 c, 502 d, and/or 502 e and the BAI 503a, 503 b, 503 c, and/or 503 d, the logical inference engine 106 cangenerate an index window 504 by taking a portion of the data model 501and mapping it into memory. The portion of the data model 501 taken intomemory can be sequential (e.g., not random). The result is a significantreduction in the size of data required to be loaded into memory.

In an aspect, bidirectional indexing using BTIs can have limits as tohow much parallelization can be applied when processing the data model501. To improve parallelization applied to the data model 501, thelogical inference engine 106 can generate bidirectional indexes forpartitions for a table in the data model 501. Such bidirectional indexesare hereinafter referred to as “indexlets.” In an aspect, the logicalinference engine 106 can generate indexlets for a given table bypartitioning the table into blocks of rows. In an aspect, the blocks ofrows can be of a same size. In an aspect, a last block of rows can be ofa size less than the remaining blocks of rows. In an aspect, afterpartitioning the blocks of rows, the logical inference engine cangenerate an indexlet for each of the blocks of rows. In an aspect,generating an indexlet for a given block of rows comprises generating abidirectional index as described above, but limited in scope to thegiven block of rows.

Provided the input data sources, the logical inference engine 106 canimplement an indexation process (e.g., symbol indexation) to generatesthe indexlets. Indexlets thus generated can serve as a foundation forproviding bi-directional indexing information for the both inferencingand/or hypercube domain calculation techniques.

Given an input data source 102 in an interpretable format, e.g., CSV,the indexation process can begin with partitioning the data source 102into disjoint, same-sized blocks of rows. In some aspects, theindexation process will not partition the last row (e.g., the size ofthe last block might be smaller than the size of the other blocks).These “slices” of the data can be then processed independently togenerate intermediate indexlet structures. Intermediate indexletstructures can be processed sequentially to generate a global symbolmap. In addition to bi-directional information (symbol to row and row tosymbol), a mapping between the symbols can reside locally in theindexlet and in the global symbol map. This mapping enables a simple yetfast and efficient transformation between symbols in an indexlet and inglobal symbol maps and vice versa through select and rank operations onbit vectors.

There are two main challenges to the indexation process: parallelizationof the creation of intermediate indexlet structures and the creation andhandling of large global symbol maps that contain potentially billionsof symbols.

The indexation process can be divided into two components: an indexerservice and a global symbol service. While the indexer service handlesan indexation request as well as distributing tasks of creating theintermediate indexlet structures, the global symbol service enablessplitting global symbol maps across machines. Even in good hash mapimplementations there is always overhead in memory consumption due tothe management of the internal data structure. As result, the ability tosplit global symbol maps across machines helps to share the load as wellas supporting both horizontal and vertical scaling when dealing withlarge data set.

In order to achieve the maximum parallelization of the creation ofintermediate indexlet structures, the indexer service can utilize adistributed computing environment. FIG. 4B illustrates an indexationservice 400. A master node 402 can comprise information regarding thecapability of worker nodes 404 a, 404 b, and 404 c registered duringtheir initialization. On receiving an indexation request, the masternode 402 distributes tasks to worker nodes 404 a, 404 b, and 404 c basedon the registered capability. In this setup, more worker nodes can bedynamically added and registered with the master node 402 to reduce therequired creation time of the intermediate indexlet structures 406 a,406 b, 406 c, 406 d, 406 e, and 406 f. Moreover, if a worker node diesduring the process, a new worker node can be instantiated and registeredto the master node 402 to take over the corresponding tasks. The masternode 402 can also communicate with a global symbol master node to getglobal symbol maps initialized and ready for the global symbol service.

When dealing with large data sets, global symbol maps can comprisebillions of symbols. Naturally, an in-memory hash map can provide betterperformance on both look up and insert operations in comparison tofile-based hash map implementations. Unfortunately, it is not practicalto have an unlimited amount of physical memory available. Althoughvirtual memory can help to elevate the limitation of physical memory,the performance of look up and insert operations degrades dramatically.

A global symbol service is provided in which global symbol maps aresplit across machines to share the load as well as the stress on memoryrequirements while achieving the desired performance.

FIG. 4C illustrates a global symbol service 410. As shown in FIG. 4B,during an initialization process, worker nodes 414 a, 414 b, and 414 cregister their capabilities, e.g., amount of memory, processing power,bandwidth, etc, with a global master node 412. On receiving anindexation request, for example from the indexer master node 402, theglobal symbol master node 412 can request initialization of globalsymbol maps 416 a, 416 b, and 416 c on worker nodes 414 a, 414 b, and414 c based on the registered capabilities. As a result, the globalsymbol maps 416 a, 416 b, and 416 c are initialized and propercapability reserved accordingly.

The indexer service and the global symbol service can generateintermediate indexlet structures and process the intermediate indexletstructures sequentially to generate the global symbol maps together withbi-directional indexing information. This constraint on processing orderpermits fast and efficient mappings between symbols that reside locallyin an indexlet and the global symbol maps. The global symbol serviceallows parallelism to improve indexation performance.

For example, a state, S, can be introduced into the global symbol maps416 a, 416 b, and 416 c on the worker nodes 414 a, 414 b, and 414 c asfollowsS={standing_by,serving,closed}where “standing_by” indicates that the global symbol map on the workernode is not in use, “serving” indicates that the global symbol map onthe worker node can be used for both look up and insert operations,“closed” indicates that the global symbol map on the worker node isfull, and, thus, only supports a look up operation.

The creation of the global symbol map can start with inserting symbolsinto a serving hash map on the corresponding worker node. When theoptimal capacity of the hash map is reached, the corresponding workernode informs the global symbol master node and changes its state toclosed. The global symbol master node can then request another workernode to handle the upcoming tasks, e.g., changing the state of hash mapfrom “standing_by” to “serving.” On subsequent processes, look upoperations can be carried out in a bulk and in a parallelized manner ona closed hash map to maximize the performance. The remaining new symbolscan then be inserted into the serving hash map on the correspondingworker node. If a worker node in “standing_by” state dies during theprocess, it can be replaced by instantiating another worker node thatregisters itself to the master node. If a worker node in “closed” or“serving” state dies, it can be replaced by either another worker nodein “standing_by” state or a newly instantiated worker node. In thiscase, the master node informs the indexer service and the range of thecorresponding data will be indexed again to reconstruct thecorresponding hash map.

In an aspect, a Bloom filter 418 a, 418 b, and 418 c can be used tofurther optimize look up performance. A Bloom filter is a probabilisticdata structure that can indicate whether an element either definitely isnot in the set or may be in the set. In other words, false positivematches are possible, but false negatives are not. The base datastructure of a Bloom filter is a bit vector. On a very large hash mapthat contains several billion symbols, the performance of the look upoperation can degrade dramatically as the size increases. The Bloomfilter is a compact data structure that can represent a set with anarbitrarily large number of elements. The Bloom filter enables fastquerying of the existence of an element in a set. Depending on theregistered resource information, the false positive rate can bespecified to achieve both the compactness of the Bloom filter and theminimum access to the hash map. A Bloom filter can improve theperformance of look up operation on closed hash map by 3 to 5 times. Theconstructed Bloom filter 418 a, 418 b, and 418 c can be used to minimizethe amount of communication required in the inferencing as well ashypercube domain construction process. Particularly, by performing lookup operations in the Bloom filters 418 a, 418 b, and 418 c first, thenumber of hash maps that possibly contain the desired information willbe minimized, and, thus, reduce the number of requests that need to betransferred through the network.

The indexer service and the global symbol service allows both local aswell as cloud-based deployment of symbol indexation. With cloud-baseddeployment, more resources can be added to improve the indexationprocess. The indexation process is bounded by the amount of resourcesand the available bandwidth. In large scale deployment, directcommunication between indexer worker nodes 404 a, 404 b, and 404 c andglobal symbol worker nodes 414 a, 414 b, and 414 c can be setup toreduce the load on the global symbol master node 412.

A representation of a data structure for indexlets is shown in FIG. 5F.Rows of a given table 550 can be divided into block bidirectionallyindexed by indexlets 552, 554 and 556, respectively. In the example ofFIG. 5F, the indexlet 552 can include pointers or references torespective columns 558, 560, and 562 as set forth above with respect tobidirectional table indexes. Each of the indexlets 552, 554, and 556 arelogically associated with a bidirectional global attribute lists 564 and566 that index a particular attribute to the blocks it is present in.Accordingly, an entry in the bidirectional global attribute list 564 and566 for a given attribute can comprise a reference to an indexletcorresponding to a block having the respective attribute. In an aspect,the reference can include a hash reference. In an aspect, as shown inFIG. 5H, an implicit relationship exists between indexlets in differenttables through a common field present in both tables and anattribute-to-attribute (A2A) index.

Thus, the logical inference engine 106 can determine a data subset basedon user selections. The logical inference engine 106 automaticallymaintains associations among every piece of data in the entire data setused in an application. The logical inference engine 106 can store thebinary state of every field and of every data table dependent on userselection (e.g., included or excluded). This can be referred to as astate space and can be updated by the logical inference engine 106 everytime a selection is made. There is one bit in the state space for everyvalue in the symbol table or row in the data table, as such the statespace is smaller than the data itself and faster to query. The inferenceengine will work associating values or binary symbols into the dimensiontuples. Dimension tuples are normally needed by a hypercube to produce aresult.

The associations thus created by the logical inference engine 106 meansthat when a user makes a selection, the logical inference engine 106 canresolve (quickly) which values are still valid (e.g., possible values)and which values are excluded. The user can continue to make selections,clear selections, and make new selections, and the logical inferenceengine 106 will continue to present the correct results from the logicalinference of those selections. In contrast to a traditional join modeldatabase, the associative model provides an interactive associativeexperience to the user.

FIG. 5C illustrates an example application of one or more BTIs. Userinput 504 can be received that impacts a selection of one or moreattribute states 506. Attribute states 506 can correspond to selectionby a user of one or more attributes (e.g., values) found in Column 1 ofTable X. In an aspect, the one or more attributes of Table X cancomprise a hash of each respective attribute. One or more BTI's 508 canbe accessed to determine one or more rows in Table X that comprise theattributes selected by the user. Row states 510 can correspond toselection of one or more rows found in Table X that comprise the one ormore selected attributes. An inverted index 512 of Column 2 can beaccessed to identify which rows of Table 1 comprise associatedattributes. Attribute states 514 for Column 2 can be updated to reflectthe associated attributes of Column 2. One or more BTI's 518 can befurther accessed to determine other associated attributes in othercolumns as needed. Attribute states 514 can be applied to other tablesvia one or more BAIs. FIG. 5D illustrates an example of relationshipsidentified by one or more BAIs.

FIG. 5E illustrates an example application of BTI's and BAI's todetermine inferred states both inter-table and intra-table using Table 1and Table 2 of FIG. 2 . A BTI 520 can be generated for the “Client”attribute of Table 2. In an aspect, the BTI 520 can comprise an invertedindex 521. In other aspect, the inverted index 521 can be considered aseparate structure. The BTI 520 can comprise a row for each uniqueattribute in the “Client” column of Table 2. Each unique attribute canbe assigned a corresponding position 522 in the BTI 520. In an aspect,the BTI 520 can comprise a hash for each unique attribute. The BTI 520can comprise a column 523 for each row of Table 2. For each attribute, a“1” can indicate the presence of the attribute in the row and a “0” canindicate an absence of the attribute from the row. “0” and “1” aremerely examples of values used to indicate presence or absence. Thus,the BTI 520 reflects that the attribute “Nisse” is found in rows 1 and 6of Table 2, the attribute “Gullan” is found in row 2 of Table 2, theattribute “Kalle” is found in rows 3 and 4 of Table 2, and the attribute“Pekka” is found in row 5 of Table 2.

The inverted index 521 can be generated such that each position in theinverted index 521 corresponds to a row of Table 2 (e.g., first positioncorresponds to row 1, second position corresponds to row 2, etc. . . .). A value can be entered into each position that reflects thecorresponding position 522 for each attribute. Thus, in the invertedindex 521, position 1 comprises the value “1” which is the correspondingposition 522 value for the attribute “Nisse”, position 2 comprises thevalue “2” which is the corresponding position 522 value for theattribute “Gullan”, position 3 comprises the value “3” which is thecorresponding position 522 value for the attribute “Kalle”, position 4comprises the value “3” which is the corresponding position 522 valuefor the attribute “Kalle”, position 5 comprises the value “4” which isthe corresponding position 522 value for the attribute “Pekka”, andposition 6 comprises the value “1” which is the corresponding position522 value for the attribute “Nisse”.

A BTI 524 can be generated for the “Product” attribute of Table 2. In anaspect, the BTI 524 can comprise an inverted index 525. In other aspect,the inverted index 525 can be considered a separate structure. The BTI524 can comprise a row for each unique attribute in the “Product” columnof Table 2. Each unique attribute can be assigned a correspondingposition 526 in the BTI 524. In an aspect, the BTI 524 can comprise ahash for each unique attribute. The BTI 524 can comprise a column 527for each row of Table 2. For each attribute, a “1” can indicate thepresence of the attribute in the row and a “0” can indicate an absenceof the attribute from the row. “0” and “1” are merely examples of valuesused to indicate presence or absence. Thus, the BTI 524 reflects thatthe attribute “Toothpaste” is found in row 1 of Table 2, the attribute“Soap” is found in rows 2, 3, and 5 of Table 2, and the attribute“Shampoo” is found in rows 4 and 6 of Table 2.

By way of example, the inverted index 525 can be generated such thateach position in the inverted index 525 corresponds to a row of Table 2(e.g., first position corresponds to row 1, second position correspondsto row 2, etc. . . . ). A value can be entered into each position thatreflects the corresponding position 526 for each attribute. Thus, in theinverted index 525, position 1 comprises the value “1” which is thecorresponding position 526 value for the attribute “Toothpaste”,position 2 comprises the value “2” which is the corresponding position526 value for the attribute “Soap”, position 3 comprises the value “2”which is the corresponding position 526 value for the attribute “Soap”,position 4 comprises the value “3” which is the corresponding position526 value for the attribute “Shampoo”, position 5 comprises the value“2” which is the corresponding position 526 value for the attribute“Soap”, and position 6 comprises the value “3” which is thecorresponding position 526 value for the attribute “Shampoo”.

By way of example, a BTI 528 can be generated for the “Product”attribute of Table 1. In an aspect, the BTI 528 can comprise an invertedindex 529. In other aspect, the inverted index 529 can be considered aseparate structure. The BTI 528 can comprise a row for each uniqueattribute in the “Product” column of Table 1. Each unique attribute canbe assigned a corresponding position 530 in the BTI 528. In an aspect,the BTI 528 can comprise a hash for each unique attribute. The BTI 528can comprise a column 531 for each row of Table 1. For each attribute, a“1” can indicate the presence of the attribute in the row and a “0” canindicate an absence of the attribute from the row. “0” and “1” aremerely examples of values used to indicate presence or absence. Thus,the BTI 528 reflects that the attribute “Soap” is found in row 1 ofTable 1, the attribute “Soft Soap” is found in row 2 of Table 1, and theattribute “Toothpaste” is found in rows 3 and 4 of Table 1.

By way of example, the inverted index 529 can be generated such thateach position in the inverted index 529 corresponds to a row of Table 1(e.g., first position corresponds to row 1, second position correspondsto row 2, etc. . . . ). A value can be entered into each position thatreflects the corresponding position 530 for each attribute. Thus, in theinverted index 529, position 1 comprises the value “1” which is thecorresponding position 530 value for the attribute “Soap”, position 2comprises the value “2” which is the corresponding position 530 valuefor the attribute “Soft Soap”, position 3 comprises the value “3” whichis the corresponding position 530 value for the attribute “Toothpaste”,and position 4 comprises the value “3” which is the correspondingposition 530 value for the attribute “Toothpaste”.

By way of example, a BAI 532 can be generated as an index between theproduct attribute of Table 2 and Table 1. The BAI 532 can comprise a rowfor each unique attribute in the BTI 524 by order of correspondingposition 526. The value in each row can comprise the correspondingposition 530 of the BTI 528. Thus, position 1 of the BAI 532 correspondsto “Toothpaste” in the BTI 524 (corresponding position 526 of 1) andcomprises the value “3” which is the corresponding position 530 for“Toothpaste” of the BTI 528. Position 2 of the BAI 532 corresponds to“Soap” in the BTI 524 (corresponding position 526 of 2) and comprisesthe value “1” which is the corresponding position 530 for “Soap” of theBTI 528. Position 3 of the BAI 532 corresponds to “Shampoo” in the BTI524 (corresponding position 526 of 3) and comprises the value “−1” whichindicates that the attribute “Shampoo” is not found in Table 1.

By way of example, a BAI 533 can be created to create an index betweenthe product attribute of Table 1 and Table 2. The BAI 533 can comprise arow for each unique attribute in the BTI 528 by order of correspondingposition 530. The value in each row can comprise the correspondingposition 526 of the BTI 524. Thus, position 1 of the BAI 533 correspondsto “Soap” in the BTI 528 (corresponding position 530 of 1) and comprisesthe value “2” which is the corresponding position 526 for “Soap” of theBTI 524. Position 2 of the BAI 533 corresponds to “Soft Soap” in the BTI528 (corresponding position 530 of 2) and comprises the value “−1” whichindicates that the attribute “Soft Soap” is not found in Table 2.Position 3 of the BAI 533 corresponds to “Toothpaste” in the BTI 528(corresponding position 530 of 3) and comprises the value “1” which isthe corresponding position 526 for “Toothpaste” of the BTI 524.

FIG. 5E illustrates an example application of the logical inferenceengine 106 utilizing the BTI 520, the BTI 524, and the BTI 528. A usercan select the “Client” “Kalle” from within a user interface. A columnfor a user selection 534 of “Kalle” can be indicated in the BTI 520comprising a value for each attribute that reflects the selection statusof the attribute. Thus, the user selection 534 comprises a value of “0”for the attribute “Nisse” indicating that “Nisse” is not selected, theuser selection 534 comprises a value of “0” for the attribute “Gullan”indicating that “Gullan” is not selected, the user selection 534comprises a value of “1” for the attribute “Kalle” indicating that“Kalle” is selected, and the user selection 534 comprises a value of “0”for the attribute “Pekka” indicating that “Pekka” is not selected.

The BTI 520 can be consulted to determine that the attribute “Kalle” hasa value of “1” in the column 523 corresponding to rows 3 and 4. In anaspect, the inverted index 521 can be consulted to determine that theuser selection 534 relates to the position 522 value of “3” which isfound in the inverted index 521 at positions 3 and 4, implicating rows 3and 4 of Table 1. Following path 535, a row state 536 can be generatedto reflect the user selection 534 as applied to the rows of Table 2. Therow state 536 can comprise a position that corresponds to each row and avalue in each position reflecting whether a row was selected. Thus,position 1 of the row state 536 comprises the value “0” indicating thatrow 1 does not contain “Kalle”, position 2 of the row state 536comprises the value “0” indicating that row 2 does not contain “Kalle”,position 3 of the row state 536 comprises the value “1” indicating thatrow 3 does contain “Kalle”, position 4 of the row state 536 comprisesthe value “1” indicating that row 4 does contain “Kalle”, position 5 ofthe row state 536 comprises the value “0” indicating that row 5 does notcontain “Kalle”, and position 6 of the row state 536 comprises the value“0” indicating that row 6 does not contain “Kalle”.

Following path 537, the row state 536 can be compared with the invertedindex 525 to determine the corresponding position 526 contained in theinverted index 525 at positions 3 and 4. The inverted index 525comprises the corresponding position 526 value of “2” in position 3 andthe corresponding position 526 value of “3” in position 4. Followingpath 538, the corresponding position 526 values of “2” and “3” can bedetermined to correspond to “Soap” and “Shampoo” respectively in the BTI524. Thus, the logical inference engine 106 can determine that both“Soap” and “Shampoo” in Table 2 are associated with “Kalle” in Table 2.The association can be reflected in an inferred state 539 in the BTI524. The inferred state 539 can comprise a column with a row for eachattribute in the BTI 524. The column can comprise a value indicated theselection state for each attribute. The inferred state 539 comprises a“0” for “Toothpaste” indicating that “Toothpaste” is not associated with“Kalle”, the inferred state 539 comprises a “1” for “Soap” indicatingthat “Soap” is associated with “Kalle”, and inferred state 539 comprisesa “1” for “Shampoo” indicating that “Shampoo” is associated with“Kalle”.

Following path 540, the inferred state 539 can be compared to the BAI532 to determine one or more associations between the selection of“Kalle” in Table 2 and one or more attributes in Table 1. As theinferred state 539 comprises a value of “1” in both position 2 andposition 3, the BAI 532 can be assessed to determine the valuescontained in position 2 and position 3 of the BAI 532 (following path541). Position 2 of the BAI 532 comprises the value “1” which identifiesthe corresponding position 530 of “Soap” and position 3 of the BAI 532comprises the value “−1” which indicates that Table 1 does not contain“Shampoo”. Thus, the logical inference engine 106 can determine that“Soap” in Table 1 is associated with “Kalle” in Table 2. The associationcan be reflected in an inferred state 542 in the BTI 528. The inferredstate 542 can comprise a column with a row for each attribute in the BTI528. The column can comprise a value indicated the selection state foreach attribute. The inferred state 542 comprises a “1” for “Soap”indicating that “Soap” is associated with “Kalle”, the inferred state542 comprises a “0” for “Soft Soap” indicating that “Soft Soap” is notassociated with “Kalle”, and the inferred state 542 comprises a “0” for“Toothpaste” indicating that “Toothpaste” is not associated with“Kalle”. Based on the current state of BTIs and BAIs, if the datasources 102 indicate that an update or delta change has occurred to theunderlying data, the BTIs and BAIs can be updated with correspondingchanges to maintain consistency.

In aspects implementing indexlets, the logical inference engine 106 canapply query language by first performing intra-table inferencing onrespective tables. Intra-table inferencing comprises transferring theimposed state of one field to other fields within the same table. In anaspect, intra-table inferencing can comprise computing the union of theindex of the active attributes in a user input 504. The intersection ofthe result of the union operation and record states (i.e. row states510) is then determined. This result is then intersected with theattribute states 514 of other columns using the inverted index 512. Ifother selection vectors from a previously provided user input vector 504has zero active entries, a conflict can be detected. In an aspect, thelogical inference engine 106 can resolve the detected conflict. In anaspect, resolving a conflict can include deleting or otherwiseeliminating one or more incompatible selections. In another aspect,resolving a conflict can include reverting the data model 501 or aportion of the data model 501, e.g. a table, record, or attribute, to aprevious state.

In an aspect, after performing intra-table inferencing, the logicalinference engine 106 can perform inter-table inferencing based on theintra-table inferencing output of a plurality of tables, as is depictedin FIG. 5G. In an aspect, intra-table inferencing can includetransferring a common field attribute of one table 569 to a child in itsbranch. In an aspect, this can be performed by running the attributestates 570 output from intra-table inferencing through anattribute-to-attribute (A2A) index 572 referencing the attribute states574 in a second table 576. In an aspect, the A2A index 572 can bepartitioned into one or more indexlets as described herein with respectto other data tables. In another aspect, transferring a common fieldattribute of one table 569 to a child in its branch by running theattribute states 570 output from intra-table inferencing through afunction or logic performing similar functionality as the A2A index 572.For example, a function, service, or other logic can accept as input apair of symbols and return an indication of whether or not they arerelated, e.g. TRUE or FALSE. In another aspect, attribute-to-attributerelations can be indicated by user input.

Based on current selections and possible rows in data tables acalculation/chart engine 108 can calculate aggregations in objectsforming transient hyper cubes in an application. The calculation/chartengine 108 can further build a virtual temporary table from whichaggregations can be made. The calculation/chart engine 108 can perform acalculation (e.g., evaluate an expression in response to a userselection/de-selection) via a multithreaded operation. The state spacecan be queried to gather all of the combinations of dimensions andvalues necessary to perform the calculation. In an aspect, the query canbe on one thread per object, one process, one worker, combinationsthereof, and the like. The expression can be calculated on multiplethreads per object. Results of the calculation can be passed to arendering engine 116 and/or optionally to an extension engine 110.

In an aspect, the chart engine 108 can receive dimensions, expressions,and sorting parameters and can compute a hypercube data structurecontaining aggregations along the dimensions. For example, a virtualrecord can be built with a placeholder for all field values (or indices)needed, as a latch memory location. When all values are assigned, thevirtual record can be processed to aggregate the fields needed forcomputations and save the dimension values in a data structure per rowof the resulting hypercube. In such a way, the traversal of the databasecan be done in an arbitrary way, just depending on requirements providedby memory consumption and indexing techniques used for the particularcase at hand. An example virtual record is shown in FIG. 5I. Thisexample considers the dimensions Order Date and Country/Region and theexpression sum(Unit Price*Quantity).

The chart engine 108 can use indices to build hypercube domains. Forexample, using bidirectional indexing, walking the database fortraversals can be performed as shown in FIG. 5J. A recursion algorithmstarts with a consistently computed state. A “forest” covering the datamodel can be constructed and the “roots” of the “trees” can be eitherthe largest table in the tree or one table containing the first neededfield. Starting from that point, the database can be (naively) traversedas follows. Initially, virtual record fields present in the currenttable can be checked and saved in a list of local fields. An activeattribute a_(i) can be taken from the input field (e.g., iteratively).Using the A2R index, the record set {r_(k)}_(k) associated to thatsymbol can be identified. The chart engine 108 can assign for everyr_(k), the virtual record entries present in the local field list. Ifthe virtual record is totally assigned, the virtual record can beprocessed. The chart engine 108 can go to next symbol. Using R2S on theoutput field, the chart engine 108 can find corresponding attributeb_(j). Using the A2A index, the chart engine 108 can translate b_(j) tothe next-in-branch table {tilde over (b)}_(j). The chart engine 108 canthen perform this same process with {tilde over (b)}_(j) as input. Whilethe preceding operations are on the global symbol maps, the impact onrespective indexlets is included in the scheduling planning.Parallelisation of hypercube traversal is also possible for commonaggregation linear operators, as described below.

The hypercube domain can be paged. The traversal can be stopped at anypoint by saving the state defined in the virtual record, and the tablesbeing traversed at that point.

Expressions are strings representing which operations to be done onfields from the database. So, consider the field set S={S₀, S₁, S₂, . .. , S_(n)} of the database. This is the union of all possible attributesper column, and those associated are included within the same set undera unique symbol. The part of an expression that is the subset of {tildeover (S)}⊂S can be used for computing the values. Furthermore, theproperty of globality of the expression, e.g., the expression requiringall records to be available, can be taken into account when evaluatingthe expression.

Provided herein is a hypercube taxonomy. Two attributes x_(i)∈S_(i),x_(j)∈S_(j) can be defined as associated if the inference engine 106 cancreate a connected path between the attributes. Such can be denoted asx_(i)˜x_(j). Consider the space of virtual records on Ŝ⊂S as

R_(v)^(Ŝ) = {r = (x₁, x₂, …, x_(k))❘x_(i) ∈ S_(i) ∧ x_(i) ∼ x_(j)∀i, j}

Subsequently, the dimension set can be defined as

Dim = R_(v)^(Ŝ)where Ŝ is the set of dimensions defined to compute the hypercube. Thisset is naturally provided with an enumeration of all its elements wherei is mapped to a unique element, denoted by Dim^(i). Furthermore,provided some expression and its corresponding computation set {tildeover (s)}, the hypercube domain can be defined as

$\chi = R_{v}^{({\overset{.}{S},\hat{S}})}$X has a natural partition

${X = {\bigcup\limits_{i}X_{i}}},{X_{i} = \{ {{{{r \in X}❘x_{j}} = {{Dim}^{i}(j)}},{j = 0},\ldots,k} \}}$

The hypercube problem can then be formulated as follows. Consider Dimand X together with I:X^(k)→R^(n), F:R^(n)→R^(p) with p=#(X). theproblem becomes computing

${( {F \circ I} )(X)} = {\sum\limits_{i}{( {f_{i} \circ I} )(X)e_{i}}}$with e_(i) the canonical vector with just 1 in i-th position, from thedata model as quickly as possible. The hypercube problems can now beclassified according to the characteristics of the expressions and therecord domains. Some cases are inherently dependent of each other whileothers are parallelisable. Classifications include, for example,a component independent operator:

${{( {F \circ I} )(X)} = {\sum\limits_{i}{( {f \circ I} )( X_{i} )e_{i}}}};$a summable operator:

${{( {F \circ I} )(X)} = {\sum\limits_{i}{\sum\limits_{x \in \mathcal{X}_{i}}{( {f \circ I} )( \{ x \} )e_{i}}}}};$anda recursive operator:

${( {f_{i + 1} \circ I} )(X)} = {{g( {\mathcal{X}_{i + 1},{\sum\limits_{j = 0}^{i}{( {f_{j} \circ I} )( X_{j} )}}} )}.}$

This algebra works with strings, if string concatenation is regarded assum.

The objective can be to minimize I/O by grouping results by indexlet,and distributing the work accordingly. The workload can be distributedconsidering the partition X_(i). Without loss of generality, thecomponent independent operator approach is disclosed and along withrelated load distribution.

In an aspect, the chart engine 108 can utilize indexlets forparallelization (e.g., distributing computations). The chart engine 108can determine one or more aggregation functions (Ŝ) and one or moreinternal functions (e.g., scalar functions) using indexlets. In anexample where an aggregation function is to be determined with respectto a single table (e.g., when fields in Ŝ belong to the same table) isdisclosed. For the partition {X_(i)}, the disclosed methods candetermine a subset {circumflex over (X)}⊂{X_(i)} that belongs to anindexlet Idx_(k). A query can be made with the respective partition inparallel for the set of all indexlets {Idx_(k)}_(k). Results can beaggregated at a top level when all queries are returned. The primarycomputational overhead comes from data transmission in this case of allpartitions as the query is executed for every indexlet and resultsreturned.

As shown in FIG. 5K, the chart engine 108 can perform the followingexample method in the case of {tilde over (S)} being contained within asingle table. The chart engine 108 can determine Dim. The chart engine108 can annotate which indexlets are needed per Dim^(i). The chartengine 108 can regroup Dim^(i) per indexlet Idx_(k). The chart engine108 assigns to every indexlet a subset Dim_(k) ⊂Dim. The chart engine108 can take {circumflex over (ƒ)} as ƒ if the function is summable anddomain independent, or its approximation {tilde over (ƒ)} if not. Thechart engine 108 can loop in k:query (in parallel) every indexletIdx_(k) with {circumflex over (ƒ)}∘I partitioned by Dim_(k). Forexample, by computing:

$F_{k} = {{\sum\limits_{i}{\sum\limits_{x \in \mathcal{X}_{i}}{( {f \circ I} )( \{ x \} ){e_{i} \cdot X_{i}}}}} \in {Dim}_{k}}$The chart engine 108 can aggregate at top level as a last pass. Forexample, by computing:

${( {F \circ I} )(X)} = {\sum\limits_{k}F_{k}}$Thus, the chart engine 108 can introduce a clear parallel pattern byevery subcomputation on indexlets.

As shown in FIG. 5L, the chart engine 108 can perform the followingexample method in the case of S being contained within multiple tables.In the multiple table case, the computational overhead is found inconstructing virtual records in parallel. The chart engine 108 candetermine Dim. The chart engine 108 can annotate which indexlets in alltables are needed per Dim^(i,j). The chart engine 108 can regroupDim^(i) per indexlet Idx_(k) ^(j) and table T_(j). The chart engine 108thus assigns a subset Dim_(k)⊂Dim to every indexlet in every table. Thechart engine 108 can identify the table with the largest number ofindexlets as a distributed master table. The chart engine 108 can passthe expression to compute as tasks to all the indexlets of the mastertable, and all other tables involved prepare the domain pieces imposedby the partitions Dim_(k) ^(j). The chart engine 108 can utilize themaster table indexlets to query the other indexlets in the closestneighbors indexlets. If the closest neighbors indexlets need furtherpieces to complete their domains, they ask downstream to theirrespective dependencies. Every indexlet of the master table thencompletes the computation of F_(k). The chart engine 108 can aggregateat top level as a last pass. For example, by computing:

${( {F \circ I} )(X)} = {\sum\limits_{k}F_{k}}$The chart engine 108 thus distributes a hypercube domain across nodes byusing the largest table to compute the domain and maximize I/O.

In the event of a data update the underlying data source/data set, a newindexlet can be generated as described above and the new indexletincorporated into the methods for evaluating an expression (single tableor multiple data). A data update can include a modification to existingdata and/or appending new data to existing data (e.g., adding data to anexisting table or adding a new table of data). Thus, the methods forevaluating an expression shown in FIG. 5K and FIG. 5L enables the systemto use the new indexlet (and any existing indexlets as needed) toevaluate an expression, thus avoiding the need to recreate thehypercube/multidimensional cube.

In an aspect, the chart engine 108 can distribute hypercube domaincomputation. For example, by finding the partitions of each indexlet perdimensions prescribed. The basic case is given when considering twoadjacent tables are associated. The chart engine 108 can utilize anindexlet centric technique instead of a symbol centric technique. Thesymbol centric traversal is done intra-indexlet. The indexlet centrictechnique is depicted in FIG. 5M.

The mapping is from indexlet to indexlet in order to establishpartitions from symbols to other symbols. This partition can bepre-computed and stored when constructing the A2A map. During theconstruction of the A2A map, the construction of the 121 map can beperformed. The map can be constructed as I2I:N→N where k is associatedto j if the indexlet Idx_(a) ^(k) of table a is associated to indexletIdx_(b) ^(j) of table b. Such a coarse mapping can be used then to labelthe records of indexlet idx_(a) ^(k) by the records of Idx_(b) ^(j) orthe symbols of the common dimension of A2A. This can be computed andstored at indexing time for A2A.

Labeling and/or coloring can be distributed per indexlet, collapsing inthe resulting indexlets, and then retransmitted to the next table usingthe I2I map. By way of example, the chart engine 108 can label allactive symbols of the dimensions involved and can select the largesttable (as measured by number of indexlets) with a column in Ŝ as a roottable. The chart engine 108 can transport the labels using the 121 mapto the root table from all tables as shown in FIG. 5M. The result isthat the different combinations of labels are then conforming everyDim_(k). In the case of multiple tables, the chart engine 108 canfurther propagate the domains to spoke tables, considering the roottable as a hub.

Optionally, the extension engine 110 can be implemented to communicatedata via an interface 112 to an external engine 114. In another aspect,the extension engine 110 can communicate data, metadata, a script, areference to one or more artificial neural networks (ANNs), one or morecommands to be executed, one or more expressions to be evaluated,combinations thereof, and the like to the external engine 114. Theinterface 114 can comprise, for example, an Application ProgrammingInterface (API). The external engine 114 can comprise one or more dataprocessing applications (e.g., simulation applications, statisticalapplications, mathematical computation applications, databaseapplications, combinations thereof, and the like). The external engine114 can be, for example, one or more of MATLAB®, R, Maple®,Mathematica®, combinations thereof, and the like.

In an aspect, the external engine 114 can be local to the associativedata indexing engine 100 or the external engine 114 can be remote fromthe associative data indexing engine 100. The external engine 114 canperform additional calculations and transmit the results to theextension engine 110 via the interface 112. A user can make a selectionin the data model of data to be sent to the external engine 114. Thelogical inference engine 106 and/or the extension engine 110 cangenerate data to be output to the external engine 114 in a format towhich the external engine 114 is accustomed to processing. In an exampleapplication, tuples forming a hypercube can comprise two dimensions andone expression, such as (Month, Year, Count (ID)), ID being a recordidentification of one entry. Then said tuples can be exchanged with theexternal engine 114 through the interface 112 as a table. If the datacomprise births there can be timestamps of the births and these can bestored as month and year. If a selection in the data model will give aset of month-year values that are to be sent out to an external unit,the logical inference engine 106 and/or the extension engine 110 canripple that change to the data model associatively and produce the data(e.g., set and/or values) that the external engine 114 needs to workwith. The set and/or values can be exchanged through the interface 112with the external engine 114. The external engine 114 can comprise anymethod and/or system for performing an operation on the set and/orvalues. In an aspect, operations on the set and/or values by theexternal engine 114 can be based on tuples (aggregated or not). In anaspect, operations on the set and/or values by the external engine 114can comprise a database query based on the tuples. Operations on the setand/or values by the external engine 114 can be anytransformation/operation of the data as long as the cardinality of theresult is consonant to the sent tuples/hypercube result.

In an aspect, tuples that are transmitted to the external engine 114through the interface 112 can result in different data being receivedfrom the external engine 114 through the interface 112. For example, atuple consisting of (Month, Year, Count (ID)) should return as 1-to-1,m-to-1 (where aggregations are computed externally) or n-to-n values. Ifdata received are not what were expected, association can be lost.Transformation of data by the external engine 114 can be configured suchthat cardinality of the results is consonant to the sent tuples and/orhypercube results. The amount of values returned can thus preserveassociativity.

Results received by the extension engine 110 from the external engine114 can be appended to the data model. In an aspect, the data can beappended to the data model without intervention of the script engine104. Data model enrichment is thus possible “on the fly.” A natural workflow is available allowing clicking users to associatively extend thedata. The methods and systems disclosed permit incorporation of userimplemented functionality into a presently used work flow. Interactionwith third party complex computation engines, such as MATLAB® or R, isthus facilitated.

The logical inference engine 106 can couple associated results to theexternal engine 114 within the context of an already processed datamodel. The context can comprise tuple or tuples defined by dimensionsand expressions computed by hypercube routines. Association is used fordetermination of which elements of the present data model are relevantfor the computation at hand. Feedback from the external engine 114 canbe used for further inference inside the inference engine or to providefeedback to the user.

A rendering engine 116 can produce a desired graphical object (charts,tables, etc) based on selections/calculations. When a selection is madeon a rendered object there can be a repetition of the process of movingthrough one or more of the logical inference engine 106, thecalculation/chart engine 108, the extension engine 110, the externalengine 114, and/or the rendering engine 116. The user can explore thescope by making different selections, by clicking on graphical objectsto select variables, which causes the graphical object to change. Atevery time instant during the exploration, there exists a current statespace, which is associated with a current selection state that isoperated on the scope (which always remains the same).

Different export features or tools 118 can be used to publish, export ordeploy any output of the associative data indexing engine 100. Suchoutput can be any form of visual representation, including, but notlimited to, textual, graphical, animation, audio, tactile, and the like.

An example database, as shown in FIG. 2 , can comprise a number of datatables (Tables 1-5). Each data table can contain data values of a numberof data variables. For example, in Table 1 each data record containsdata values of the data variables “Product,” “Price,” and “Part.” Ifthere is no specific value in a field of the data record, this field isconsidered to hold a NULL-value. Similarly, in Table 2 each data recordcontains values of the variables “Date,” “Client,” “Product,” and“Number.” In Table 3 each data record contains values of variable “Date”as “Year,” “Month” and “Day.” In Table 4 each data record containsvalues of variables “Client” and “Country,” and in Table 5 each datarecord contains values of variables “Country,” “Capital,” and“Population.” Typically, the data values are stored in the form ofASCII-coded strings, but can be stored in any form.

The methods provided can be implemented by means of a computer programas illustrated in a flowchart of a method 300 in FIG. 3 . In a step 302,the program can read some or all data records in the database, forinstance using a SELECT statement which selects all the tables of thedatabase, e.g. Tables 1-5. In an aspect, the database can be read intoprimary memory of a computer.

To increase evaluation speed, each unique value of each data variable insaid database can be assigned a different binary code and the datarecords can be stored in binary-coded form. This can be performed, forexample, when the program first reads the data records from thedatabase. For each input table, the following steps can be carried out.The column names, e.g. the variables, of the table can be read (e.g.,successively). Every time a new data variable appears, a data structurecan be instantiated for the new data variable. An internal tablestructure can be instantiated to contain some or all the data records inbinary form, whereupon the data records can be read (e.g., successively)and binary-coded. For each data value, the data structure of thecorresponding data variable can be checked to establish if the value haspreviously been assigned a binary code. If so, that binary code can beinserted in the proper place in the above-mentioned table structure. Ifnot, the data value can be added to the data structure and assigned anew binary code, for example the next binary code in ascending order,before being inserted in the table structure. In other words, for eachdata variable, a unique binary code can be assigned to each unique datavalue.

After having read some or all data records in the database, the method300 can analyze the database in a step 304 to identify all connectionsbetween the data tables. A connection between two data tables means thatthese data tables have one variable in common. In an aspect, step 304can comprise generation of one or more bidirectional table indexes andone or more bidirectional associative indexes. In an aspect, generationof one or more bidirectional table indexes and one or more bidirectionalassociative indexes can comprise a separate step. In another aspect,generation of one or more bidirectional table indexes and one or morebidirectional associative indexes can be on demand. After the analysis,all data tables are virtually connected. In FIG. 2 , such virtualconnections are illustrated by double ended arrows. The virtuallyconnected data tables can form at least one so-called “snowflakestructure,” a branching data structure in which there is one and onlyone connecting path between any two data tables in the database. Thus, asnowflake structure does not contain any loops. If loops do occur amongthe virtually connected data tables, e.g. if two tables have more thanone variable in common, a snowflake structure can in some cases still beformed by means of special algorithms known in the art for resolvingsuch loops.

After this initial analysis, the user can explore the database. In doingso, the user defines in step 306 a mathematical function, which could bea combination of mathematical expressions. Assume that the user wants toextract the total sales per year and client from the database in FIG. 2. The user defines a corresponding mathematical function “SUM (x*y)”,and selects the calculation variables to be included in this function:“Price” and “Number.” The user also selects the classificationvariables: “Client” and “Year.”

The method 300 then identifies in step 308 all relevant data tables,e.g. all data tables containing any one of the selected calculation andclassification variables, such data tables being denoted boundarytables, as well as intermediate data tables in the connecting path(s)between these boundary tables in the snowflake structure, such datatables being denoted connecting tables. There are no connecting tablesin the present example. In an aspect, one or more bidirectional tableindexes and one or more bidirectional associative indexes can beaccessed as part of step 308.

In the present example, all occurrences of every value, e.g. frequencydata, of the selected calculation variables can be included forevaluation of the mathematical function. In FIG. 2 , the selectedvariables (“Price,” “Number”) can require such frequency data. Now, asubset (B) can be defined that includes all boundary tables (Tables 1-2)containing such calculation variables and any connecting tables betweensuch boundary tables in the snowflake structure. It should be noted thatthe frequency requirement of a particular variable is determined by themathematical expression in which it is included.

Determination of an average or a median calls for frequency information.In general, the same is true for determination of a sum, whereasdetermination of a maximum or a minimum does not require frequency dataof the calculation variables. It can also be noted that classificationvariables in general do not require frequency data.

Then, a starting table can be selected in step 310, for example, amongthe data tables within subset (B). In an aspect, the starting table canbe the data table with the largest number of data records in thissubset. In FIG. 2 , Table 2 can be selected as the starting table. Thus,the starting table contains selected variables (“Client,” “Number”), andconnecting variables (“Date,” “Product”). These connecting variableslink the starting table (Table 2) to the boundary tables (Tables 1 and3).

Thereafter, a conversion structure can be built in step 312. Thisconversion structure can be used for translating each value of eachconnecting variable (“Date,” “Product”) in the starting table (Table 2)into a value of a corresponding selected variable (“Year,” “Price”) inthe boundary tables (Table 3 and 1, respectively). A table of theconversion structure can be built by successively reading data recordsof Table 3 and creating a link between each unique value of theconnecting variable (“Date”) and a corresponding value of the selectedvariable (“Year”). It can be noted that there is no link from value 4(“Date: 1999 Jan. 12”), since this value is not included in the boundarytable. Similarly, a further table of the conversion structure can bebuilt by successively reading data records of Table 1 and creating alink between each unique value of the connecting variable (“Product”)and a corresponding value of the selected variable (“Price”). In thisexample, value 2 (“Product: Toothpaste”) is linked to two values of theselected variable (“Price: 6.5”), since this connection occurs twice inthe boundary table. Thus, frequency data can be included in theconversion structure. Also note that there is no link from value 3(“Product: Shampoo”).

When the conversion structure has been built, a virtual data record canbe created. Such a virtual data record accommodates all selectedvariables (“Client,” “Year,” “Price,” “Number”) in the database. Inbuilding the virtual data record, a data record is read in step 314 fromthe starting table (Table 2). Then, the value of each selected variable(“Client”, “Number”) in the current data record of the starting tablecan be incorporated in the virtual data record in a step 316. Also, byusing the conversion structure each value of each connecting variable(“Date”, “Product”) in the current data record of the starting table canbe converted into a value of a corresponding selected variable (“Year”,“Price”), this value also being incorporated in the virtual data record.

In step 318 the virtual data record can be used to build an intermediatedata structure. Each data record of the intermediate data structure canaccommodate each selected classification variable (dimension) and anaggregation field for each mathematical expression implied by themathematical function. The intermediate data structure can be builtbased on the values of the selected variables in the virtual datarecord. Thus, each mathematical expression can be evaluated based on oneor more values of one or more relevant calculation variables in thevirtual data record, and the result can be aggregated in the appropriateaggregation field based on the combination of current values of theclassification variables (“Client,” “Year”).

The above procedure can be repeated for one or more additional (e.g.,all) data records of the starting table. In a step 320 it can be checkedwhether the end of the starting table has been reached. If not, theprocess can be repeated from step 314 and further data records can beread from the starting table. Thus, an intermediate data structure canbe built by successively reading data records of the starting table, byincorporating the current values of the selected variables in a virtualdata record, and by evaluating each mathematical expression based on thecontent of the virtual data record. If the current combination of valuesof classification variables in the virtual data record is new, a newdata record can be created in the intermediate data structure to holdthe result of the evaluation. Otherwise, the appropriate data record israpidly found, and the result of the evaluation is aggregated in theaggregation field.

Thus, data records can be added to the intermediate data structure asthe starting table is traversed. The intermediate data structure can bea data table associated with an efficient index system, such as an AVLor a hash structure. The aggregation field can be implemented as asummation register, in which the result of the evaluated mathematicalexpression is accumulated.

In some aspects, e.g. when evaluating a median, the aggregation fieldcan be implemented to hold all individual results for a uniquecombination of values of the specified classification variables. Itshould be noted that only one virtual data record is needed in theprocedure of building the intermediate data structure from the startingtable. Thus, the content of the virtual data record can be updated foreach data record of the starting table. This can minimize the memoryrequirement in executing the computer program.

After traversing the starting table, the intermediate data structure cancontain a plurality of data records. If the intermediate data structureaccommodates more than two classification variables, the intermediatedata structure can, for each eliminated classification variable, containthe evaluated results aggregated over all values of this classificationvariable for each unique combination of values of remainingclassification variables.

When the intermediate data structure has been built, a final datastructure, e.g., a multidimensional cube, as shown in non-binarynotation in Table 6 of FIG. 4A, can be created in a step 322 byevaluating the mathematical function (“SUM (x*y)”) based on the resultsof the mathematical expression (“x*y”) contained in the intermediatedata structure. In doing so, the results in the aggregation fields foreach unique combination of values of the classification variables can becombined. In the example, the creation of the final data structure isstraightforward, due to the trivial nature of the present mathematicalfunction. The content of the final data structure can be presented tothe user, for example in a two-dimensional table, in step 324, as shownin Table 7 of FIG. 4A. Alternatively, if the final data structurecontains many dimensions, the data can be presented in a pivot table, inwhich the user can interactively move up and down in dimensions, as iswell known in the art.

In an aspect, step 322 can involve any of the processes describedpreviously with regard to FIG. 5A through FIG. 5M as part of a processfor creating the hypercube/multidimensional cube. For example, outputfrom the logical inference engine 18 and/or 106 utilizing one or moreBTIs and or one or more A2A indexes can be used in creation of thehypercube/multidimensional cube. When a user makes a selection, theinference engine 18 and/or 106 calculates a data subset of which one ormore BTIs and/or A2A indexes can be generated and provided to the chartengine 58 and/or 108 for use in generating a hypercube/multidimensionalcube and/or evaluating one or more expressions against ahypercube/multidimensional cube via one or more BTIs and/or A2A indexesas described with regard to FIG. 5A through FIG. 5M.

At step 326, input from the user can be received. For example, inputform the user can be a selection and/or de-selection of the presentedresults.

Optionally, input from the user at step 326 can comprise a request forexternal processing. In an aspect, the user can be presented with anoption to select one or more external engines to use for the externalprocessing. Optionally, at step 328, data underlying the user selectioncan be configured (e.g., formatted) for use by an external engine.Optionally, at step 330, the data can be transmitted to the externalengine for processing and the processed data can be received. Thereceived data can undergo one or more checks to confirm that thereceived data is in a form that can be appended to the data model. Forexample, one or more of an integrity check, a format check, acardinality check, combinations thereof, and the like. Optionally, atstep 332, processed data can be received from the external engine andcan be appended to the data model as described herein. In an aspect, thereceived data can have a lifespan that controls how long the receiveddata persists with the data model. For example, the received data can beincorporated into the data model in a manner that enables a user toretrieve the received data at another time/session. In another example,the received data can persist only for the current session, making thereceived data unavailable in a future session.

FIG. 5A illustrates how a selection 50 operates on a scope 52 ofpresented data to generate a data subset 54. The data subset 54 can forma state space, which is based on a selection state given by theselection 50. In an aspect, the selection state (or “user state”) can bedefined by a user clicking on list boxes and graphs in a user interfaceof an application. An application can be designed to host a number ofgraphical objects (charts, tables, etc.) that evaluate one or moremathematical functions (also referred to as an “expression”) on the datasubset 54 for one or more dimensions (classification variables). Theresult of this evaluation creates a chart result 56 which can be amultidimensional cube which can be visualized in one or more of thegraphical objects.

The application can permit a user to explore the scope 52 by makingdifferent selections, by clicking on graphical objects to selectvariables, which causes the chart result 56 to change. At every timeinstant during the exploration, there exists a current state space,which can be associated with a current selection state that is operatedon the scope 52 (which always remains the same).

As illustrated in FIG. 5A, when a user makes a selection, the inferenceengine 18 calculates a data subset. Also, an identifier ID1 for theselection together with the scope can be generated based on the filtersin the selection and the scope. Subsequently, an identifier ID2 for thedata subset is generated based on the data subset definition, forexample a bit sequence that defines the content of the data subset. ID2can be put into a cache using ID1 as a lookup identifier. Likewise, thedata subset definition can be put in the cache using ID2 as a lookupidentifier.

As shown in FIG. 5A, a chart calculation in a calculation/chart engine58 takes place in a similar way. Here, there are two information sets:the data subset 54 and relevant chart properties 60. The latter can be,but not restricted to, a mathematical function together with calculationvariables and classification variables (dimensions). Both of theseinformation sets can be used to calculate the chart result 56, and bothof these information sets can be also used to generate identifier ID3for the input to the chart calculation. ID2 can be generated already inthe previous step, and ID3 can be generated as the first step in thechart calculation procedure.

The identifier ID3 can be formed from ID2 and the relevant chartproperties. ID3 can be seen as an identifier for a specific chartgeneration instance, which can include all information needed tocalculate a specific chart result. In addition, a chart resultidentifier ID4 can be created from the chart result definition, forexample a bit sequence that defines the chart result 56. ID4 can be putin the cache using ID3 as a lookup identifier. Likewise, the chartresult definition can be put in the cache using ID4 as a lookupidentifier.

Optionally, further calculations, transforming, and/or processing can beincluded through an extension engine 62. Optionally, associated resultsfrom the inference engine 18 and further computed by hypercubecomputation in said calculation/chart engine 58 can be coupled to anexternal engine 64 that can comprise one or more data processingapplications (e.g., simulation applications, statistical applications,mathematical computation applications, database applications,combinations thereof, and the like). Context of a data model processedby the inference engine 18 can comprise a tuple or tuples of valuesdefined by dimensions and expressions computed by hypercube routines.Data can be exchanged through an interface 66.

The associated results coupled to the external engine 64 can beintermediate. Further results that can be final hypercube results canalso be received from the external engine 64. Further results can be fedback to be included in the Data/Scope 52 and enrich the data model. Thefurther results can also be rendered directly to the user in the chartresult 56. Data received from and computed by the external engine 64 canbe used for further associative discovery.

Each of the data elements of the database shown in Tables 1-5 of FIG. 2has a data element type and a data element value (for example “Client”is the data element type and “Nisse” is the data element value).Multiple records can be stored in different database structures such asdata cubes, data arrays, data strings, flat files, lists, vectors, andthe like; and the number of database structures can be greater than orequal to one and can comprise multiple types and combinations ofdatabase structures. While these and other database structures can beused with, and as part of, the methods and systems disclosed, theremaining description will refer to tables, vectors, strings and datacubes solely for convenience.

Additional database structures can be included within the databaseillustrated as an example herein, with such structures includingadditional information pertinent to the database such as, in the case ofproducts for example; color, optional packages, etc. Each table cancomprise a header row which can identify the various data element types,often referred to as the dimensions or the fields, that are includedwithin the table. Each table can also have one or more additional rowswhich comprise the various records making up the table. Each of the rowscan contain data element values (including null) for the various dataelement types comprising the record.

The database as referred to in Tables 1-5 of FIG. 2 can be queried byspecifying the data element types and data element values of interestand by further specifying any functions to apply to the data containedwithin the specified data element types of the database. The functionswhich can be used within a query can include, for example, expressionsusing statistics, sub-queries, filters, mathematical formulas, and thelike, to help the user to locate and/or calculate the specificinformation wanted from the database. Once located and/or calculated,the results of a query can be displayed to the user with variousvisualization techniques and objects such as list boxes of a userinterface illustrated in FIG. 6 .

The graphical objects (or visual representations) can be substantiallyany display or output type including graphs, charts, trees,multi-dimensional depictions, images (computer generated or digitalcaptures), video/audio displays describing the data, hybridpresentations where output is segmented into multiple display areashaving different data analysis in each area and so forth. A user canselect one or more default visual representations; however, a subsequentvisual representation can be generated on the basis of further analysisand subsequent dynamic selection of the most suitable form for the data.

In an aspect, a user can select a data point and a visualizationcomponent can instantaneously filter and re-aggregate other fields andcorresponding visual representations based on the user's selection. Inan aspect, the filtering and re-aggregation can be completed withoutquerying a database. In an aspect, a visual representation can bepresented to a user with color schemes applied meaningfully. Forexample, a user selection can be highlighted in green, datasets relatedto the selection can be highlighted in white, and unrelated data can behighlighted in gray. A meaningful application of a color scheme providesan intuitive navigation interface in the state space.

The result of a standard query can be a smaller subset of the datawithin the database, or a result set, which is comprised of the records,and more specifically, the data element types and data element valueswithin those records, along with any calculated functions, that matchthe specified query. For example, as indicated in FIG. 6 , the dataelement value “Nisse” can be specified as a query or filtering criteriaas indicated by a frame in the “Client” header row. In some aspects, theselected element can be highlighted in green. By specifically selecting“Nisse,” other data element values in this row are excluded as shown bygray areas. Further, “Year” “1999” and “Month” “Jan” are selected in asimilar way.

Optionally, in this application, external processing can also berequested by ticking “External” in the user interface of FIG. 6 . Dataas shown in FIG. 7 can be exchanged with an External engine 64 throughthe interface 66 of FIG. 5A. In addition to evaluating the mathematicalfunction (“SUM (Price*Number)”) based on the results of the mathematicalexpression (“Price*Number”) contained in the intermediate data structurethe mathematical function (“SUM (ExtFunc(Price*Number))”) can beevaluated. Data sent out are (Nisse, 1999, Jan, {19.5, null}). In thiscase the external engine 64 can process data in accordance with theformula

if (x==null)

-   -   y=0.5

else

-   -   y=x        as shown in in FIG. 7 . The result input through the interface        66 will be (19.5, 0.5) as reflected in the graphical        presentation in FIG. 6 .

In a further aspect, external processing can also be optionallyrequested by ticking “External” in a box as shown in FIG. 8 . Data asshown in FIG. 9 can be exchanged with an external engine 64 through theInterface 66 of FIG. 5A. In addition to evaluating the mathematicalfunction (“SUM(Price*Number)”) based on the results of the mathematicalexpression (“Price*Number”) contained in the intermediate data structurethe mathematical functionSUM(ExtFunc(Price*Number))can be evaluated. Data sent out are (Nisse, 1999, Jan, {19.5, null}). Inthis case the external engine 64 will process data in accordance withFunction (1) as shown below and in FIG. 9 . The result input through theInterface 66 will be (61.5) as reflected in the graphical presentationin FIG. 8 .

y=ExtAggr(x[ ])

-   -   for (x in x[ ])        -   if (x==null)            -   y=y+42        -   else            -   y=y+x    -   Function (1)

A further optional embodiment is shown in FIG. 10 and FIG. 11 . The samebasic data as in previous examples apply. A user selects “Pekka,”“1999,” “Jan,” and “External.” By selecting “External,” alreadydetermined and associated results are coupled to the external engine 64.Feedback data from the external engine 64 based on an externalcomputation, ExtQualification(Sum(Price*Number)), as shown in FIG. 13will be the information “MVG.” This information can be fed back to thelogical inference engine 18. The information can also be fed back to thegraphical objects of FIG. 10 and as a result a qualification table 68will highlight “MVG” (illustrated with a frame in FIG. 10 ). Othervalues (U, G, and VG) are shown in gray areas. The result input throughthe Interface 66 will be Soap with a value of 75 as reflected in thegraphical presentation (bar chart) of FIG. 10 . FIG. 11 is a schematicrepresentation of data exchanged with an external engine based onselections in FIG. 10 . FIG. 12 is a table showing results fromcomputations based on different selections in the presentation of FIG.10 .

Should a user instead select “Gullan,” “1999,” “Jan,” and “External,”the feedback signal would include “VG” based on the content shown inqualification table 68. The computations actually performed in theexternal engine 62 are not shown or indicated, since they are notrelevant to the inference engine.

In FIG. 13 a user has selected “G” as depicted by 70 in thequalification table 68. As a result information fed back from theexternal engine 64 to the external engine 62 and further to theinference engine 18 the following information will be highlighted:“Nisse,” “1999,” and “Jan” as shown in FIG. 13 . Furthermore, the resultproduced will be Soap 37.5 as reflected in the graphical presentation(bar chart) of FIG. 13 .

In an aspect, illustrated in FIG. 14 provided is a method 1400 forreceiving, at a master node, capability information associated with aplurality of worker nodes at 1402. The method 1400 can comprisereceiving, at the master node, an indexation request at 1404. The method1400 can comprise, in response to the indexation request, distributingone or more tasks to the plurality of worker nodes based on therespective capability information, wherein the one or more tasks relateto generating a plurality of indexlets at 1406.

The method 1400 can further comprise dynamically receiving at the masternode, additional capability information from additional worker nodes.The method 1400 can further comprise determining a generation time forthe plurality of indexlets exceeds a threshold. The method 1400 canfurther comprise distributing one or more tasks to the additional workernodes in response to the generation time exceeding the threshold. Themethod 1400 can further comprise determining that one of the pluralityof worker nodes is unavailable. The method 1400 can further compriseinstantiating a new worker node and distributing the one or more taskspreviously assigned to the one of the plurality of worker nodes to thenew worker node. Receiving, at the master node, the capabilityinformation associated with the plurality of worker nodes can compriseperforming an initialization process wherein the plurality of workernodes register with the master node.

The method 1400 can further comprise transmitting, by the master node, aglobal symbol map initialization request to a global symbol master node.In an aspect, illustrated in FIG. 15 provided is a method 1500 forreceiving, at a global symbol master node, capability informationassociated with a plurality of worker nodes at 1502. The method 1500 cancomprise receiving, at the global symbol master node, an indexationrequest at 1504. The method 1500 can comprise, in response to theindexation request, distributing one or more tasks to the plurality ofworker nodes based on the respective capability information, wherein theone or more tasks relate to generating a plurality of global symbol mapsat 1506.

The method 1500 can further comprise dynamically receiving at the masternode, additional capability information from additional worker nodes.The method 1500 can further comprise determining a generation time forthe plurality of global symbol maps exceeds a threshold. The method 1500can further comprise distributing one or more tasks to the additionalworker nodes in response to the generation time exceeding the threshold.The method 1500 can further comprise determining that one of theplurality of worker nodes is unavailable. The method 1500 can furthercomprise instantiating a new worker node and distributing the one ormore tasks previously assigned to the one of the plurality of workernodes to the new worker node. Receiving, at the global symbol masternode, the capability information associated with the plurality of workernodes can comprise performing an initialization process wherein theplurality of worker nodes register with the global symbol master node.The method 1500 can further comprise receiving an indication from one ofthe plurality of worker nodes related to a status of a respective globalsymbol map processed by the one of the plurality of worker nodes. Thestatus can indicate that the respective global symbol map on the one ofthe plurality of worker nodes is not in use. The status can indicatethat the respective global symbol map on the one of the plurality ofworker nodes can be used for both look up and insert operations. Thestatus can indicate that the respective global symbol map on the one ofthe plurality of worker nodes is full.

In an exemplary aspect, the methods and systems can be implemented on acomputer 1601 as illustrated in FIG. 16 and described below. Similarly,the methods and systems disclosed can utilize one or more computers toperform one or more functions in one or more locations. FIG. 16 is ablock diagram illustrating an exemplary operating environment forperforming the disclosed methods. This exemplary operating environmentis only an example of an operating environment and is not intended tosuggest any limitation as to the scope of use or functionality ofoperating environment architecture. Neither should the operatingenvironment be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary operating environment.

The present methods and systems can be operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that can be suitable for use with the systems andmethods comprise, but are not limited to, personal computers, servercomputers, laptop devices, and multiprocessor systems. Additionalexamples comprise set top boxes, programmable consumer electronics,network PCs, minicomputers, mainframe computers, distributed computingenvironments that comprise any of the above systems or devices, and thelike.

The processing of the disclosed methods and systems can be performed bysoftware components. The disclosed systems and methods can be describedin the general context of computer-executable instructions, such asprogram modules, being executed by one or more computers or otherdevices. Generally, program modules comprise computer code, routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Thedisclosed methods can also be practiced in grid-based and distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules can be located inboth local and remote computer storage media including memory storagedevices.

Further, one skilled in the art will appreciate that the systems andmethods disclosed herein can be implemented via a general-purposecomputing device in the form of a computer 1601. The components of thecomputer 1601 can comprise, but are not limited to, one or moreprocessors 1603, a system memory 1612, and a system bus 1613 thatcouples various system components including the one or more processors1603 to the system memory 1612. The system can utilize parallelcomputing.

The system bus 1613 represents one or more of several possible types ofbus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, or local bus using any ofa variety of bus architectures. The bus 1613, and all buses specified inthis description can also be implemented over a wired or wirelessnetwork connection and each of the subsystems, including the one or moreprocessors 1603, a mass storage device 1604, an operating system 1605,software 1606, data 1607, a network adapter 1608, the system memory1612, an Input/Output Interface 1610, a display adapter 1609, a displaydevice 1611, and a human machine interface 1602, can be contained withinone or more remote computing devices 1614 a,b,c at physically separatelocations, connected through buses of this form, in effect implementinga fully distributed system.

The computer 1601 typically comprises a variety of computer readablemedia. Exemplary readable media can be any available media that isaccessible by the computer 1601 and comprises, for example and not meantto be limiting, both volatile and non-volatile media, removable andnon-removable media. The system memory 1612 comprises computer readablemedia in the form of volatile memory, such as random access memory(RAM), and/or non-volatile memory, such as read only memory (ROM). Thesystem memory 1612 typically contains data such as the data 1607 and/orprogram modules such as the operating system 1605 and the software 1606that are immediately accessible to and/or are presently operated on bythe one or more processors 1603.

In another aspect, the computer 1601 can also comprise otherremovable/non-removable, volatile/non-volatile computer storage media.By way of example, FIG. 16 illustrates the mass storage device 1604which can provide non-volatile storage of computer code, computerreadable instructions, data structures, program modules, and other datafor the computer 1601. For example and not meant to be limiting, themass storage device 1604 can be a hard disk, a removable magnetic disk,a removable optical disk, magnetic cassettes or other magnetic storagedevices, flash memory cards, CD-ROM, digital versatile disks (DVD) orother optical storage, random access memories (RAM), read only memories(ROM), electrically erasable programmable read-only memory (EEPROM), andthe like.

Optionally, any number of program modules can be stored on the massstorage device 1604, including by way of example, the operating system1605 and the software 1606. Each of the operating system 1605 and thesoftware 1606 (or some combination thereof) can comprise elements of theprogramming and the software 1606. The data 1607 can also be stored onthe mass storage device 1604. The data 1607 can be stored in any of oneor more databases known in the art. Examples of such databases comprise,DB2®, Microsoft® Access, Microsoft® SQL Server, Oracle®, mySQL,PostgreSQL, and the like. The databases can be centralized ordistributed across multiple systems.

In an aspect, the software 1606 can comprise one or more of a scriptengine, a logical inference engine, a calculation engine, an extensionengine, and/or a rendering engine. In an aspect, the software 1606 cancomprise an external engine and/or an interface to the external engine.

In another aspect, the user can enter commands and information into thecomputer 1601 via an input device (not shown). Examples of such inputdevices comprise, but are not limited to, a keyboard, pointing device(e.g., a “mouse”), a microphone, a joystick, a scanner, tactile inputdevices such as gloves, and other body coverings, and the like These andother input devices can be connected to the one or more processors 1603via the human machine interface 1602 that is coupled to the system bus1613, but can be connected by other interface and bus structures, suchas a parallel port, game port, an IEEE 1394 Port (also known as aFirewire port), a serial port, or a universal serial bus (USB).

In yet another aspect, the display device 1611 can also be connected tothe system bus 1613 via an interface, such as the display adapter 1609.It is contemplated that the computer 1601 can have more than one displayadapter 1609 and the computer 1601 can have more than one display device1611. For example, the display device 1611 can be a monitor, an LCD(Liquid Crystal Display), or a projector. In addition to the displaydevice 1611, other output peripheral devices can comprise componentssuch as speakers (not shown) and a printer (not shown) which can beconnected to the computer 1601 via the Input/Output Interface 1610. Anystep and/or result of the methods can be output in any form to an outputdevice. Such output can be any form of visual representation, including,but not limited to, textual, graphical, animation, audio, tactile, andthe like. The display device 1611 and computer 1601 can be part of onedevice, or separate devices.

The computer 1601 can operate in a networked environment using logicalconnections to one or more remote computing devices 1614 a,b,c. By wayof example, a remote computing device can be a personal computer,portable computer, smartphone, a server, a router, a network computer, apeer device or other common network node, and so on. Logical connectionsbetween the computer 1601 and a remote computing device 1614 a,b,c canbe made via a network 1615, such as a local area network (LAN) and/or ageneral wide area network (WAN). Such network connections can be throughthe network adapter 1608. The network adapter 1608 can be implemented inboth wired and wireless environments. In an aspect, one or more of theremote computing devices 1614 a,b,c can comprise an external engineand/or an interface to the external engine.

For purposes of illustration, application programs and other executableprogram components such as the operating system 1605 are illustratedherein as discrete blocks, although it is recognized that such programsand components reside at various times in different storage componentsof the computing device 1601, and are executed by the one or moreprocessors 1603 of the computer. An implementation of the software 1606can be stored on or transmitted across some form of computer readablemedia. Any of the disclosed methods can be performed by computerreadable instructions embodied on computer readable media. Computerreadable media can be any available media that can be accessed by acomputer. By way of example and not meant to be limiting, computerreadable media can comprise “computer storage media” and “communicationsmedia.” “Computer storage media” comprise volatile and non-volatile,removable and non-removable media implemented in any methods ortechnology for storage of information such as computer readableinstructions, data structures, program modules, or other data. Exemplarycomputer storage media comprises, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by a computer.

The methods and systems can employ Artificial Intelligence techniquessuch as machine learning and iterative learning. Examples of suchtechniques include, but are not limited to, expert systems, case basedreasoning, Bayesian networks, behavior based AI, neural networks, fuzzysystems, evolutionary computation (e.g. genetic algorithms), swarmintelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g.Expert inference rules generated through a neural network or productionrules from statistical learning).

While the methods and systems have been described in connection withpreferred embodiments and specific examples, it is not intended that thescope be limited to the particular embodiments set forth, as theembodiments herein are intended in all respects to be illustrativerather than restrictive.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order. Accordingly, where a method claim doesnot actually recite an order to be followed by its steps or it is nototherwise specifically stated in the claims or descriptions that thesteps are to be limited to a specific order, it is in no way intendedthat an order be inferred, in any respect. This holds for any possiblenon-express basis for interpretation, including: matters of logic withrespect to arrangement of steps or operational flow; plain meaningderived from grammatical organization or punctuation; the number or typeof embodiments described in the specification.

It will be apparent to those skilled in the art that variousmodifications and variations can be made without departing from thescope or spirit. Other embodiments will be apparent to those skilled inthe art from consideration of the specification and practice disclosedherein. It is intended that the specification and examples be consideredas exemplary only, with a true scope and spirit being indicated by thefollowing claims.

The invention claimed is:
 1. A method comprising: receiving, by a firstworker node of a plurality of worker nodes, from a master node, a firsttask associated with a data model; generating, by the first worker node,based on the first task, a first symbol map that bidirectionally indexesa first portion of the data model; receiving, by a second worker node ofthe plurality of worker nodes, from the master node, a second task,wherein the second task is sent by the master node based on adetermination that the first worker node is unavailable; and generating,by the second worker node, based on the second task, a second symbol mapthat bidirectionally indexes at least one of: the first portion of thedata model or a second portion of the data model.
 2. The method of claim1, wherein the first symbol map comprises a first state associated withthe first worker node.
 3. The method of claim 2, wherein the first stateis indicative of the first worker node being in a stand-by state, andwherein the method further comprises: causing, by the master node, basedon the first state, the second worker node to be instantiated;receiving, by the master node from the second worker node, capabilityinformation associated with the second worker node; and sending, by themaster node to the second worker node, based on the capabilityinformation and the determination that the first worker node isunavailable, the second task, wherein the second symbol mapbidirectionally indexes the second portion of the data model.
 4. Themethod of claim 2, wherein the first state is indicative of the firstworker node being in either a serving state or a closed state, andwherein the method further comprises at least one of: causing, by themaster node, based on the first state, the second worker node to beinstantiated; or causing, by the master node, based on the first state,the second worker node to generate the second symbol map, wherein thesecond symbol map bidirectionally indexes the first portion of the datamodel.
 5. The method of claim 1, further comprising: inserting, by thefirst worker node, a plurality of symbols into the first symbol map;determining, by the first worker node, that the first symbol map hasreached an optimal capacity limit; and sending, by the first workernode, to the master node, an indication that the first symbol map hasreached the optimal capacity limit.
 6. The method of claim 5, furthercomprising: receiving, by the master node from the first worker node,the indication that the first symbol map has reached the optimalcapacity limit; and sending, to the second worker node, the second task.7. The method of claim 1, further comprising: receiving, by the masternode, an indexation request associated with the data model; receiving,by the master node from the plurality of worker nodes, capabilityinformation associated with each worker node of the plurality of workernodes; and determining, by the master node, based on the capabilityinformation, the first task and the second task.
 8. A method comprising:receiving, by a master node, an indexation request associated with adata model; sending, to a first worker node of a plurality of workernodes, a first task that causes the first worker node to generate afirst symbol map, wherein the first symbol map bidirectionally indexes afirst portion of the data model; determining that the first worker nodeis unavailable for at least one of: a lookup operation or an insertoperation; and sending, to a second worker node of the plurality ofworker nodes, a second task that causes the second worker node togenerate a second symbol map, wherein the second symbol mapbidirectionally indexes at least one of: the first portion of the datamodel or a second portion of the data model.
 9. The method of claim 8,wherein the lookup operation comprises processing at least part of aquery associated with the first symbol map, and wherein the insertoperation comprises inserting a plurality of symbols into the firstsymbol map.
 10. The method of claim 8, wherein the first symbol mapcomprises a first state associated with the first worker node.
 11. Themethod of claim 10, wherein the first state is indicative of the firstworker node being in a stand-by state, and wherein the method furthercomprises: causing, based on the first state, the second worker node tobe instantiated.
 12. The method of claim 11, further comprising:receiving, from the second worker node, capability informationassociated with the second worker node; and sending, to the secondworker node, based on the capability information and the determinationthat the first worker node is unavailable, the second task, wherein thesecond symbol map bidirectionally indexes the second portion of the datamodel.
 13. The method of claim 10, wherein the first state is indicativeof the first worker node being in either a serving state or a closedstate, and wherein the second symbol map bidirectionally indexes thefirst portion of the data model.
 14. The method of claim 8, whereindetermining that the first worker node is unavailable for at least oneof: the lookup operation or the insert operation comprises: receiving,from the first worker node, an indication that the first symbol map hasreached an optimal capacity limit.
 15. A method comprising: receiving,by a first worker node of a plurality of worker nodes, from a masternode, a first task associated with a data model; generating, based onthe first task, a first symbol map that bidirectionally indexes a firstportion of the data model; determining that the first symbol map hasreached an optimal capacity limit; and sending, to the master node, anindication that the first symbol map has reached the optimal capacitylimit, wherein the indication causes the master node to send a secondtask to a second worker node of the plurality of worker nodes.
 16. Themethod of claim 15, wherein determining that the first symbol map hasreached the optimal capacity limit comprises: inserting a plurality ofsymbols into the first symbol map; and determining, based on theplurality of symbols, that the first symbol map has reached the optimalcapacity limit.
 17. The method of claim 15, further comprising:receiving, by the master node from the first worker node, the indicationthat the first symbol map has reached the optimal capacity limit; andsending, to the second worker node, the second task.
 18. The method ofclaim 15, further comprising: receiving, by the second worker node, fromthe master node, the second task; and generating, by the second workernode, based on the second task, a second symbol map that bidirectionallyindexes at least one of the first portion of the data model or a secondportion of the data model.
 19. The method of claim 15, wherein the firstsymbol map comprises a first state associated with the first worker nodeindicative of the first worker node being in a stand-by state, andwherein the method further comprises: causing, by the master node, basedon the first state, the second worker node to be instantiated;receiving, by the master node from the second worker node, capabilityinformation associated with the second worker node; and sending, by themaster node to the second worker node, based on the capabilityinformation and the determination that the first worker node isunavailable, the second task, wherein the second symbol mapbidirectionally indexes a second portion of the data model.
 20. Themethod of claim 15, wherein the first symbol map comprises a first stateassociated with the first worker node indicative of the first workernode being in a stand-by state, and wherein the method further comprisesat least one of: causing, by the master node, based on the first state,the second worker node to be instantiated; or causing, by the masternode, based on the first state, the second worker node to generate asecond symbol map, wherein the second symbol map bidirectionally indexesthe first portion of the data model.